More importantly, for visually evoked emotion decoding, neuroscience research studies have revealed the discrepancy in emotional expression between the left and right hemispheres despite the common visual input [1]}, [2]} which can be regarded as the prior knowledge for developing models. Many previous studies have proven that exploiting the bi-hemisphere discrepancy is capable of improving the performance of emotion decoding by using electroencephalogram (EEG) based data [3]}, [4]}. They employ independent networks to learn features for the left and right hemispheres, and then fuse them for further decoding task. Inspired by these, we regard the left and right hemispheres, and their difference as multiple information resources (views) to explore the interaction between the two hemispheres and take advantage of a Product of Experts (PoE) mechanism [5]} for feature level fusion.
